Machine Learning Regression Masterclass in Python
What you’ll learn
Master Python programming and Scikit learn as applied to machine learning regression
Understand the underlying theory behind simple and multiple linear regression techniques
Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
Apply multiple linear regression to predict stock prices and Universities acceptance rate
Cover the basics and underlying theory of polynomial regression
Apply polynomial regression to predict employees’ salary and commodity prices
Understand the theory behind logistic regression
Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
Understand the underlying theory and mathematics behind Artificial Neural Networks
Learn how to train network weights and biases and select the proper transfer functions
Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test
Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
Sample real-world, practical projects
Requirements
Machine Learning basics
PC with Internet connetion
Description
Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries.Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns.The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including:· Simple Linear Regression· Multiple Linear Regression· Polynomial Regression· Logistic Regression· Decision trees regression· Ridge Regression· Lasso Regression· Artificial Neural Networks for Regression analysis· Regression Key performance indicatorsThe course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.
Overview
Section 1: INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]
Lecture 1 Course Welcome Message
Lecture 2 Updates on Udemy Reviews
Lecture 3 Course Overview
Lecture 4 BONUS: Learning Path
Lecture 5 ML vs. DL vs. AI
Lecture 6 Get the materials
Section 2: ANACONDA AND JUPYTER INSTALLATION
Lecture 7 Download and Set up Anaconda
Lecture 8 What is Jupiter Notebook
Section 3: SIMPLE LINEAR REGRESSION
Lecture 9 Intro to Simple Linear Regression
Lecture 10 Simple Linear Regression Intuition
Lecture 11 Least Squares
Lecture 12 Project #1 – Overview
Lecture 13 Project #1 – Data Visualization
Lecture 14 Project #1 – Divide Data into Training and Testing
Lecture 15 Project #1 – Train Model
Lecture 16 Project #1 – Test Model
Lecture 17 Project #2 – Overview
Lecture 18 Project #2 – Solution
Lecture 19 Project #2 – Visualization
Lecture 20 Project #2 – Prepare Training and Testing Data
Lecture 21 Project #2 – Test Model
Lecture 22 Project #2 – Model Testing
Section 4: REGRESSION KEY PERFORMANCE INDICATORS
Lecture 23 Regression Metrics Intro
Lecture 24 Regression Metric Part 1
Lecture 25 Regression Metric Part 2
Lecture 26 Bias Variance Tradeoff
Section 5: POLYNOMIAL REGRESSION
Lecture 27 Polynomial Regression Intro
Lecture 28 Polynomial Regression – Intuition
Lecture 29 Poly Regression – Salary Load Data
Lecture 30 Poly Regression – Visualize Data
Lecture 31 Poly Regression – Linear Trainingtesting
Lecture 32 Poly Regression – Poly Part 1
Lecture 33 Poly Regression – Poly Part 2
Lecture 34 Poly Regression Project 2 Overview
Lecture 35 Poly Regression – Economies Linear -1
Lecture 36 Poly Regression – Economies Linear -2
Lecture 37 Poly Regression – Economies Poly
Section 6: MULTIPLE LINEAR REGRESSION
Lecture 38 Multiple Linear Regression Intro
Lecture 39 Multiple Linear Regression Overview
Lecture 40 Project #1 – Load Data and Libraries
Lecture 41 Project #1 – Data Visualization
Lecture 42 Project #1 – Model Training and Evaluation
Lecture 43 Project #1 – Model Results Evaluation
Lecture 44 Project #2 – Overview
Lecture 45 Project #2 – Load Data
Lecture 46 Project #2 – Data Visualization
Lecture 47 Project #2 – Train the Model
Lecture 48 Project #2 – Model Evaluation
Lecture 49 Project #2 – Retraining Model
Section 7: LOGISTIC REGRESSION
Lecture 50 Logistic Regression Intro
Lecture 51 Logistic Regression Intuition
Lecture 52 Confusion Matrix
Lecture 53 Project #2 – Data Import
Lecture 54 Project #2 – Visualization
Lecture 55 Project #2 – Data Cleaning
Lecture 56 Project #2 – Training Testing
Lecture 57 Model Testing Visualization
Section 8: APPLY ARTIFICIAL NEURAL NETWORKS TO PERFORM REGRESSION TASKS
Lecture 58 Artificial Neural Networks Intro
Lecture 59 Theory Part 1
Lecture 60 Theory Part 2
Lecture 61 Theory Part 3
Lecture 62 Theory Part 4
Lecture 63 Theory Part 5
Lecture 64 Theory Part 6
Lecture 65 Project – Load Dataset
Lecture 66 Project – Visualize Dataset
Lecture 67 Scale the Data
Lecture 68 Train the Model
Lecture 69 Evaluate the Model
Lecture 70 Multiple Linear regression
Lecture 71 Model Improvement with more features
Section 9: LASSO AND RIDGE REGRESSION
Lecture 72 Ridge and Lasso Intro
Lecture 73 Ridge Lasso Part 1
Lecture 74 Ridge Lasso Part 2
Lecture 75 Ridge Lasso Part 3
Lecture 76 Ridge and Lasso in Practice
Section 10: Bonus Lectures
Lecture 77 ***YOUR SPECIAL BONUS***
Data Scientists who want to apply their knowledge on Real World Case Studies,Machine Learning Enthusiasts who look to add more projects to their Portfolio
Course Information:
Udemy | English | 10h 21m | 5.11 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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